SoroushVahidi/scidocs huggingface dataset:
Social Media Security Culture The Human Dimension in Social Media Management
(2012)

Social media provides both opportunities and risks for any organization. Secure integration of social media platforms in organizational ICT infrastructures tends to be focused mainly on technical aspects. Social media security management usually ignores the human dimension, but protection can only be achieved through a holistic approach. Social media security culture must be part of the overall organizational culture. From a survey conducted to determine social media guidelines, a management model was developed for creating, monitoring and controlling social media security culture. The management model will be mapped to an assessment and reporting tool.
Retrograde amnesia and memory consolidation: a neurobiological perspective
(1995)

The fact that information acquired before the onset of amnesia can be lost (retrograde amnesia) has fascinated psychologists, biologists, and clinicians for over 100 years. Studies of retrograde amnesia have led to the concept of memory consolidation, whereby medial temporal lobe structures direct the gradual establishment of memory representations in neocortex. Recent theoretical accounts have inspired a simple neural network model that produces behavior consistent with experimental data and makes these ideas about memory consolidation more concrete. Recent physiological and anatomical findings provide important information about how memory consolidation might actually occur.
The International Society for Biomechanics ) 5 July 1993 THE USE OF SURFACE ELECTROMYOGRAPHY IN BIOMECHANICS
(2002)

................................................................................................................... 2 INTRODUCTION ............................................................................................................. 3 FACTORS AFFECTING THE EMG SIGNAL AND FORCE PRODUCED BY A MUSCLE.............. 5 DETECTION AND PROCESSING OF THE EMG SIGNAL.................................................... 10 THE ACTIVATION TIMING OF MUSCLES......................................................................... 12 THE FORCE / EMG SIGNAL RELATIONSHIP .................................................................. 17 THE EMG SIGNAL AS A FATIGUE INDEX ...................................................................... 25 SUMMARY OF RECOMMENDATIONS.............................................................................. 32 SUMMARY OF PROBLEMS FOR RESOLUTION................................................................. 34 ISSUES FOR INTERNATIONAL AGREEMENT.................................................................... 36 ACKNOWLEDGEMENTS ................................................................................................ 37 © DelSys Incorporated
Enhancing recurrent neural network-based language models by word tokenization
(2018)

Introduction Statistical language models estimate the probability for a given sequence of words. Given a sentence s with n words such as s = (w1,w2 . . .wn), the language model assigns P(s). Statistical language models assess good word sequence estimations based on the sequence probability estimation. Building robust, fast and accurate language models is one of the main factors in the success of building systems such as machine translation systems and automatic speech recognition systems. Statistical language models can be estimated based on various approaches. Classical language models are estimated based on n-gram word sequences such as P(s) = P(w1,w2 . . .wn) = ∏n i=1 P(wi|wi−1) and can be approximated based on the Markov concept for shorter contexts (for example, bigram if n = 2 or trigram if n = 3 and so on). Recent researchers have applied neural networks different architectures to build and estimate language models. The classical feed-forward neural network-based language models have been continuously reporting good results among the traditional n-gram language modeling techniques [1]. It Abstract
The relationship between trauma and beliefs about hearing voices: a study of psychiatric and non-psychiatric voice hearers.
(2008)

BACKGROUND
Cognitive models suggest that distress associated with auditory hallucinations is best understood in terms of beliefs about voices. What is less clear is what factors govern such beliefs. This study aimed to explore the way in which traumatic life events contribute towards beliefs about voices and any associated distress.


METHOD
The difference in the nature and prevalence of traumatic life events and associated psychological sequelae was compared in two groups of voice hearers: psychiatric voice hearers with predominantly negative beliefs about voices (PVH) and non-psychiatric voice hearers with predominantly positive beliefs about voices (NPVH). The data from the two groups were then combined in order to examine which factors could significantly account for the variance in beliefs about voices and therefore levels of distress.


RESULTS
Both groups reported a high prevalence of traumatic life events although significantly more PVH reported trauma symptoms sufficient for a diagnosis of post-traumatic stress disorder (PTSD). Furthermore, significantly more PVH reported experiencing childhood sexual abuse. Current trauma symptoms (re-experiencing, avoidance and hyperarousal) were found to be a significant predictor of beliefs about voices. Trauma variables accounted for a significant proportion of the variance in anxiety and depression.


CONCLUSIONS
The results suggest that beliefs about voices may be at least partially understood in the context of traumatic life events.
Universal Planning Networks
(2018)

A key challenge in complex visuomotor control is learning abstract representations that are effective for specifying goals, planning, and generalization. To this end, we introduce universal planning networks (UPN). UPNs embed differentiable planning within a goal-directed policy. This planning computation unrolls a forward model in a latent space and infers an optimal action plan through gradient descent trajectory optimization. The plan-by-gradient-descent process and its underlying representations are learned end-to-end to directly optimize a supervised imitation learning objective. We find that the representations learned are not only effective for goal-directed visual imitation via gradient-based trajectory optimization, but can also provide a metric for specifying goals using images. The learned representations can be leveraged to specify distance-based rewards to reach new target states for model-free reinforcement learning, resulting in substantially more effective learning when solving new tasks described via imagebased goals. Visit https://sites.google. com/view/upn-public/home for video highlights.
ADE: A Framework for Robust Complex Robotic Architectures
(2006)

Robots that can interact naturally with humans require the integration and coordination of many different components with heavy computational demands. We argue that an architecture framework with facilities for dynamic, reliable, fault-recovering, remotely accessible, distributed computing is needed for the development and operation of applications that support and enhance human activities and capabilities. We describe a robotic architecture development system, called ADE, that is built on top of a multi-agent system in order to provide all of the above features. Specifically, we discuss support for autonomic computing in ADE, briefly comparing it to related features of other commonly used robotic systems. We also report our experiences with ADE in the development of an architecture for an intelligent robot assistant and provide experimental results demonstrating the system's utility
Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos
(2010)

There has recently been a great deal of interest in the development of algorithms that objectively measure the integrity of video signals. Since video signals are being delivered to human end users in an increasingly wide array of applications and products, it is important that automatic methods of video quality assessment (VQA) be available that can assist in controlling the quality of video being delivered to this critical audience. Naturally, the quality of motion representation in videos plays an important role in the perception of video quality, yet existing VQA algorithms make little direct use of motion information, thus limiting their effectiveness. We seek to ameliorate this by developing a general, spatio-spectrally localized multiscale framework for evaluating dynamic video fidelity that integrates both spatial and temporal (and spatio-temporal) aspects of distortion assessment. Video quality is evaluated not only in space and time, but also in space-time, by evaluating motion quality along computed motion trajectories. Using this framework, we develop a full reference VQA algorithm for which we coin the term the MOtion-based Video Integrity Evaluation index, or MOVIE index. It is found that the MOVIE index delivers VQA scores that correlate quite closely with human subjective judgment, using the Video Quality Expert Group (VQEG) FRTV Phase 1 database as a test bed. Indeed, the MOVIE index is found to be quite competitive with, and even outperform, algorithms developed and submitted to the VQEG FRTV Phase 1 study, as well as more recent VQA algorithms tested on this database.
Visibility of wavelet quantization noise
(1997)

The discrete wavelet transform (DWT) decomposes an image into bands that vary in spatial frequency and orientation. It is widely used for image compression, measures of the visibility of DWT quantization errors are required to achieve optimal compression. Uniform quantization of a single band of coefficients results in an artifact that we call DWT uniform quantization noise; it is the sum of a lattice of random amplitude basis functions of the corresponding DWT synthesis filter. We measured visual detection thresholds for samples of DWT uniform quantization noise in Y, Cb, and Cr color channels. The spatial frequency of a wavelet is r2/sup -/spl lambda//, where r is the display visual resolution in pixels/degree, and /spl lambda/ is the wavelet level. Thresholds increase rapidly with wavelet spatial frequency. Thresholds also increase from Y to Cr to Cb, and with orientation from lowpass to horizontal/vertical to diagonal. We construct a mathematical model for DWT noise detection thresholds that is a function of level, orientation, and display visual resolution. This allows calculation of a "perceptually lossless" quantization matrix for which all errors are in theory below the visual threshold. The model may also be used as the basis for adaptive quantization schemes.
Image features that draw fixations
(2003)

The ability to automatically detect ‘visually interesting’ regions in an image has many practical applications especially in the design of active machine vision systems. This paper describes a data-driven approach that uses eye tracking in tandem with principal component analysis to extract lowlevel image features that attract human gaze. Data analysis on an ensemble of image patches extracted at the observer’s point of gaze revealed features that resemble derivatives of the 2D Gaussian operator. Dissimilarities between human and random fixations are investigated by comparing the features extracted at the point of gaze to the general image structure obtained by random sampling in monte-carlo simulations. Finally, a simple application where these features are used to predict fixations is illustrated.
ObliviStore: High Performance Oblivious Cloud Storage
(2013)

We design and build ObliviStore, a high performance, distributed ORAM-based cloud data store secure in the malicious model. To the best of our knowledge, ObliviStore is the fastest ORAM implementation known to date, and is faster by 10X or more in comparison with the best known ORAM implementation. ObliviStore achieves high throughput by making I/O operations asynchronous. Asynchrony introduces security challenges, i.e., we must prevent information leakage not only through access patterns, but also through timing of I/O events. We propose various practical optimizations which are key to achieving high performance, as well as techniques for a data center to dynamically scale up a distributed ORAM. We show that with 11 trusted machines (each with a modern CPU), and 20 Solid State Drives, ObliviStore achieves a throughput of 31.5MB/s with a block size of 4KB.
Neural Network Implementation Using CUDA and OpenMP
(2008)

Many algorithms for image processing and pattern recognition have recently been implemented on GPU (graphic processing unit) for faster computational times. However, the implementation using GPU encounters two problems. First, the programmer should master the fundamentals of the graphics shading languages that require the prior knowledge on computer graphics. Second, in a job which needs much cooperation between CPU and GPU, which is usual in image processings and pattern recognitions contrary to the graphics area, CPU should generate raw feature data for GPU processing as much as possible to effectively utilize GPU performance. This paper proposes more quick and efficient implementation of neural networks on both GPU and multi-core CPU. We use CUDA (compute unified device architecture) that can be easily programmed due to its simple C language-like style instead of GPU to solve the first problem. Moreover, OpenMP (Open Multi-Processing) is used to concurrently process multiple data with single instruction on multi-core CPU, which results ineffectively utilizing the memories of GPU. In the experiments, we implemented neural networks-based text detection system using the proposed architecture, and the computational times showed about 15 times faster than implementation using CPU and about 4 times faster than implementation on only GPU without OpenMP.
An empirical evaluation of internet latency expansion
(2004)

The Internet's latency expansion determines the asymptotic performance of large-scale distributed systems (such as Peer-to-Peer systems), but previous studies on the Internet have defined expansion in terms of router-level hops. In this paper, we empirically determine the Internet's latency expansion characteristics using measurements from two different Internet topology datasets. Our results show that the Internet router-level topology exhibits a power-law latency expansion, in contrast to its exponential expansion rate in terms of hops.
From Cloud to Fog Computing: A Review and a Conceptual Live VM Migration Framework
(2017)

Fog computing, an extension of cloud computing services to the edge of the network to decrease latency and network congestion, is a relatively recent research trend. Although both cloud and fog offer similar resources and services, the latter is characterized by low latency with a wider spread and geographically distributed nodes to support mobility and real-time interaction. In this paper, we describe the fog computing architecture and review its different services and applications. We then discuss security and privacy issues in fog computing, focusing on service and resource availability. Virtualization is a vital technology in both fog and cloud computing that enables virtual machines (VMs) to coexist in a physical server (host) to share resources. These VMs could be subject to malicious attacks or the physical server hosting it could experience system failure, both of which result in unavailability of services and resources. Therefore, a conceptual smart pre-copy live migration approach is presented for VM migration. Using this approach, we can estimate the downtime after each iteration to determine whether to proceed to the stop-and-copy stage during a system failure or an attack on a fog computing node. This will minimize both the downtime and the migration time to guarantee resource and service availability to the end users of fog computing. Last, future research directions are outlined.
On image classification: city images vs. landscapes
(1998)

Grouping images into semantically meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Based on these groupings, eeective indices can be built for an image database. In this paper, we show how a speciic high-level classiication problem (city images vs. landscapes) can be solved from relatively simple low-level features geared for the particular classes. We have developed a procedure to qualitatively measure the saliency of a feature towards a classiication problem based on the plot of the intra-class and inter-class distance distributions. We use this approach to determine the discriminative power of the following features: color histogram, color coherence vector, DCT coeecient, edge direction histogram, and edge direction coherence vector. We determine that the edge direction-based features have the most discriminative power for the classiication problem of interest here. A weighted k-NN classiier is used for the classiication which results in an accuracy of 93:9% when evaluated on an image database of 2; 716 images using the leave-one-out method. This approach has been extended to further classify 528 landscape images into forests, mountains, and sunset/sunrise classes. First, the input images are classiied as sunset/sunrise images vs. forest & mountain images (94:5% accuracy) and then the forest & mountain images are classiied as forest images or mountain images (91:7% accuracy). We are currently identifying further semantic classes to assign to images as well as extracting low level features which are salient for these classes. Our nal goal is to combine multiple 2-class classiiers into a single hierarchical classiier.
Programming by choice: urban youth learning programming with scratch
(2008)

This paper describes Scratch, a visual, block-based programming language designed to facilitate media manipulation for novice programmers. We report on the Scratch programming experiences of urban youth ages 8-18 at a Computer Clubhouse 'an after school center' over an 18-month period. Our analyses of 536 Scratch projects collected during this time documents the learning of key programming concepts even in the absence of instructional interventions or experienced mentors. We discuss the motivations of urban youth who choose to program in Scratch rather than using one of the many other software packages available to them and the implications for introducing programming at after school settings in underserved communities.
HyperFlex: An SDN virtualization architecture with flexible hypervisor function allocation
(2015)

Network Virtualization (NV) and Software-Defined Networking (SDN) are both expected to increase the flexibility and programmability of today's communication networks. Combining both approaches may even be a further step towards increasing the efficiency of network resource utilization. Multiple solutions for virtualizing SDN networks have already been proposed, however, they are either implemented in software or they require special network hardware. We propose HyperFlex, an SDN hypervisor architecture that relies on the decomposition of the hypervisor into functions that are essential for virtualizing SDN networks. The hypervisor functions can be flexibly executed in software or hosted on SDN network elements. Furthermore, existing hypervisor solutions focus on data-plane virtualization mechanisms and neglect the virtualization of the control-plane of SDN networks. HyperFlex provides control-plane virtualization by adding a control-plane isolation function, either in software or on network elements. The isolation function ensures that the resources of the control-plane are shared correctly between each virtual SDN network while it also protects the hypervisor resources from resource exhaustion.
MapReduce-based fuzzy c-means clustering algorithm: implementation and scalability
(2015)

The management and analysis of big data has been identified as one of the most important emerging needs in recent years. This is because of the sheer volume and increasing complexity of data being created or collected. Current clustering algorithms can not handle big data, and therefore, scalable solutions are necessary. Since fuzzy clustering algorithms have shown to outperform hard clustering approaches in terms of accuracy, this paper investigates the parallelization and scalability of a common and effective fuzzy clustering algorithm named Fuzzy C-Means (FCM) algorithm. The algorithm is parallelized using the MapReduce paradigm outlining how the Map and Reduce primitives are implemented. A validity analysis is conducted in order to show that the implementation works correctly achieving competitive purity results compared to state-of-the art clustering algorithms. Furthermore, a scalability analysis is conducted to demonstrate the performance of the parallel FCM implementation with increasing number of computing nodes used.
Design and implementation of FROST: Digital forensic tools for the OpenStack cloud computing platform
(2013)

We describe the design, implementation, and evaluation of FROSTdthree new forensic tools for the OpenStack cloud platform. Our implementation for the OpenStack cloud platform supports an Infrastructure-as-a-Service (IaaS) cloud and provides trustworthy forensic acquisition of virtual disks, API logs, and guest firewall logs. Unlike traditional acquisition tools, FROST works at the cloud management plane rather than interacting with the operating system inside the guest virtual machines, thereby requiring no trust in the guest machine. We assume trust in the cloud provider, but FROST overcomes nontrivial challenges of remote evidence integrity by storing log data in hash trees and returning evidence with cryptographic hashes. Our tools are user-driven, allowing customers, forensic examiners, and law enforcement to conduct investigations without necessitating interaction with the cloud provider. We demonstrate how FROST’s new features enable forensic investigators to obtain forensically-sound data from OpenStack clouds independent of provider interaction. Our preliminary evaluation indicates the ability of our approach to scale in a dynamic cloud environment. The design supports an extensible set of forensic objectives, including the future addition of other data preservation, discovery, real-time monitoring, metrics, auditing, and acquisition capabilities. a 2013 Josiah Dykstra and Alan T. Sherman. Published by Elsevier Ltd. All rights reserved.